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Non-negative low rank and sparse graph for semi-supervised learning

Liansheng Zhuang, Haoyuan Gao, Zhouchen Lin, Yi Ma, Xin Zhang, Nenghai Yu
2012 2012 IEEE Conference on Computer Vision and Pattern Recognition  
This paper proposes a novel non-negative low-rank and sparse (NNLRS) graph for semisupervised learning.  ...  We demonstrate the effectiveness of NNLRS-graph in semi-supervised classification and discriminative analysis.  ...  Conclusion This paper proposes a novel informative graph, called the nonnegative low rank and sparse graph (NNLRS-graph), for graph-based semi-supervised learning.  ... 
doi:10.1109/cvpr.2012.6247944 dblp:conf/cvpr/ZhuangGLMZY12 fatcat:4c6ibn6ijnbs7ga3giivxpl7fi

Constructing a Nonnegative Low-Rank and Sparse Graph With Data-Adaptive Features

Liansheng Zhuang, Shenghua Gao, Jinhui Tang, Jingjing Wang, Zhouchen Lin, Yi Ma, Nenghai Yu
2015 IEEE Transactions on Image Processing  
Index Terms-Graph Construction, low-rank and sparse representation, semi-supervised learning, data embedding.  ...  First, we propose to build a nonnegative low-rank and sparse (referred to as NNLRS) graph for the given data representation.  ...  CONCLUSION This paper proposes a novel informative graph, i.e., nonnegative low-rank and sparse graph (NNLRS-graph), for graph-based semi-supervised learning.  ... 
doi:10.1109/tip.2015.2441632 pmid:26057712 fatcat:jhe7svay5zgpnf4bqgssh7xnq4

Semi-Supervised Classification Based on Low Rank Representation

Xuan Hou, Guangjun Yao, Jun Wang
2016 Algorithms  
In this paper, we take advantage of low-rank representation for graph construction and propose an inductive semi-supervised classifier called Semi-Supervised Classification based on Low-Rank Representation  ...  Graph-based semi-supervised classification uses a graph to capture the relationship between samples and exploits label propagation techniques on the graph to predict the labels of unlabeled samples.  ...  Author Contributions: Xuan Hou and Guangjun Yao performed experiments and drafted the manuscript; Jun Wang proposed the idea and conceived the whole process and revised the manuscript; All the authors  ... 
doi:10.3390/a9030048 fatcat:lyxvww5tw5fh7mzz543faem6ne

Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning

Yong Peng, Bao-Liang Lu, Suhang Wang
2015 Neural Networks  
Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL).  ...  In this paper, we propose an enhanced LRR via sparse manifold adaption, termed manifold low-rank representation (MLRR), to learn low-rank data representation.  ...  Non-negative low-rank and sparse (NNLRS) graph (Zhuang, Gao, Lin, Ma, Zhang and Yu, 2012) was proposed by imposing the non-negative and sparse constraints on the low-rank representation coefficient.  ... 
doi:10.1016/j.neunet.2015.01.001 pmid:25634552 fatcat:xmy7g3owfza6vnw63vafnbomvu

Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features [article]

Liansheng Zhuang, Shenghua Gao, Jinhui Tang, Jingjing Wang, Zhouchen Lin, Yi Ma
2014 arXiv   pre-print
Firstly, we propose to build a non-negative low-rank and sparse (referred to as NNLRS) graph for the given data representation.  ...  This paper aims at constructing a good graph for discovering intrinsic data structures in a semi-supervised learning setting.  ...  CONCLUSION This paper proposes a novel informative graph, called the nonnegative low rank and sparse graph (NNLRS-graph), for graph-based semi-supervised learning.  ... 
arXiv:1409.0964v1 fatcat:rxgmo3denbfyvfmbjtxinwyzdu

Sparse semi-supervised learning on low-rank kernel

Kai Zhang, Qiaojun Wang, Liang Lan, Yu Sun, Ivan Marsic
2014 Neurocomputing  
In this paper, we introduce L 1 -norm penalization on the low-rank factorized kernel for efficient, globally optimal model selection in graph-based semi-supervised learning.  ...  Of particular interest is the semi-supervised learning, where very few training samples are available among large volumes of unlabeled data.  ...  In [28, 17] , a sparse adjacency graph is built up by sparse regression of the sample coordinates and then used for semi-supervised learning.  ... 
doi:10.1016/j.neucom.2013.09.033 fatcat:2ukch2jzh5eupa6fmgm2pn6b54

Combining graph embedding and sparse regression with structure low-rank representation for semi-supervised learning

Cong-Zhe You, Vasile Palade, Xiao-Jun Wu
2016 Complex Adaptive Systems Modeling  
Abstract In this paper, we propose a novel method for semi-supervised learning by combining graph embedding and sparse regression, termed as graph embedding and sparse regression with structure low rank  ...  Most of the graph based semi-supervised learning methods take into account the local neighborhood information while ignoring the global structure of the data.  ...  Acknowledgements The authors would like to thank the anonymous reviewers and editors for their valuable suggestions. Competing interests The authors declare that they have no competing interests.  ... 
doi:10.1186/s40294-016-0034-7 fatcat:lfspsce7bfeapfffawobaq7kwy

Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation

Xiaozhao Fang, Yong Xu, Xuelong Li, Zhihui Lai, Wai Keung Wong
2016 IEEE Transactions on Cybernetics  
We also explicitly impose the sparse constraint on the affinity matrix such that the affinity matrix obtained by NNLRR is non-negative low-rank and sparse.  ...  The affinity matrix is obtained by seeking a non-negative low-rank matrix that represents each data sample as a linear combination of others.  ...  Non-negative low-rank and sparse (NNLRS) [26] graph for semi-supervised learning learns the weights of edges in graph by seeking a nonnegative low-rank and sparse matrix that represents each data point  ... 
doi:10.1109/tcyb.2015.2454521 pmid:26259210 fatcat:bu4kthpjmrej3dmx3lppehmday

Distributed Low-Rank Subspace Segmentation

Ameet Talwalkar, Lester Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan
2013 2013 IEEE International Conference on Computer Vision  
Vision problems ranging from image clustering to motion segmentation to semi-supervised learning can naturally be framed as subspace segmentation problems, in which one aims to recover multiple low-dimensional  ...  Moreover, past work aimed at scaling up low-rank matrix factorization is not applicable to LRR given its non-decomposable constraints.  ...  task of graph-based semi-supervised learning.  ... 
doi:10.1109/iccv.2013.440 dblp:conf/iccv/TalwalkarMMCJ13 fatcat:qgjtuqnlsjhdhfh7mxpqpzq764

Structure Preserving Low-Rank Representation for Semi-supervised Face Recognition [chapter]

Yong Peng, Suhang Wang, Shen Wang, Bao-Liang Lu
2013 Lecture Notes in Computer Science  
Constructing an informative and discriminative graph plays an important role in the graph based semi-supervised learning methods.  ...  Among these graph construction methods, low-rank representation based graph, which calculates the edge weights of both labeled and unlabeled samples as the low-rank representation (LRR) coefficients, has  ...  for semi-supervised learning.  ... 
doi:10.1007/978-3-642-42042-9_19 fatcat:zfp3k66a2rge7dpcri5yyzb6my

Leveraging Pattern Semantics for Extracting Entities in Enterprises

Fangbo Tao, Bo Zhao, Ariel Fuxman, Yang Li, Jiawei Han
2015 Proceedings of the 24th International Conference on World Wide Web - WWW '15  
Sparse internal signals are the only source for discovering them.  ...  the majority of sparse enterprise entities, while using more low-precision patterns in sparse setting also introduces noise drastically.  ...  Ranking entities and adding top confident ones into seeds. A semi-supervised framework is used to extract more low-precision/sparse patterns.  ... 
doi:10.1145/2736277.2741670 pmid:26705540 pmcid:PMC4688019 dblp:conf/www/TaoZFLH15 fatcat:gayhxkdhznfzbdj3xzb6td7eue

Semi-supervised Learning based on Bayesian Networks and Optimization for Interactive Image Retrieval

M. Yang, J. Guan, G. Qiu, K. Lam
2006 Procedings of the British Machine Vision Conference 2006  
In this work, we show that this semi-supervised learning method can be naturally adopted as a computational tool to incorporate users feedbacks for interactive image retrieval.  ...  In this paper, we present a novel interactive image retrieval technique using semi-supervised learning.  ...  Relevance feedback for the 2 nd round and 3 rd round of interactions is based on user selection of 4 positive and 4 negative samples as labeled data for the semi-supervised learning.  ... 
doi:10.5244/c.20.99 dblp:conf/bmvc/YangGQL06 fatcat:4744qpme2bhr7n4bamlexikcwq

Semi-supervised ranking on very large graphs with rich metadata

Bin Gao, Tie-Yan Liu, Wei Wei, Taifeng Wang, Hang Li
2011 Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11  
Specifically, we define a semi-supervised learning framework for ranking of nodes on a very large graph and derive within our proposed framework an efficient algorithm called Semi-Supervised PageRank.  ...  This paper addresses the problem and proposes a general framework as well as an efficient algorithm for graph ranking.  ...  CONCLUSIONS AND FUTURE WORK In this paper, we have defined a semi-supervised learning framework for graph ranking on a very-large-scale graph, and developed an efficient algorithm named Semi-Supervised  ... 
doi:10.1145/2020408.2020430 dblp:conf/kdd/GaoLWWL11 fatcat:bpasrgwlavanjfdpnh5b6jdd7q

SMACD: Semi-supervised Multi-Aspect Community Detection [chapter]

Ekta Gujral, Evangelos E. Papalexakis
2018 Proceedings of the 2018 SIAM International Conference on Data Mining  
An orthogonal line of work, broadly construed as semi-supervised learning, approaches the problem by introducing a small percentage of node assignments to communities and propagates that knowledge throughout  ...  To the best of our knowledge, SMACD is the first approach to incorporate multiaspect graph information and semi-supervision, while being able to discover communities.  ...  ACKNOWLEDGEMENTS The authors would like to thank Evrim Acar for discussions on the NNSCMTF model, Vladimir Gligorijevic for sharing code, and Xiaowen Dong for sharing the MIT dataset.  ... 
doi:10.1137/1.9781611975321.79 dblp:conf/sdm/GujralP18 fatcat:to7o563zsfclbinutcozymxiz4

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing [article]

Danfeng Hong and Wei He and Naoto Yokoya and Jing Yao and Lianru Gao and Liangpei Zhang and Jocelyn Chanussot and Xiao Xiang Zhu
2021 arXiv   pre-print
For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications.  ...  Compared to the convex models, non-convex modeling, which is capable of characterizing more complex real scenes and providing the model interpretability technically and theoretically, has been proven to  ...  An et al. [76] attempted to learn the low-dimensional tensorized HS representations on a sparse and low-rank graph.  ... 
arXiv:2103.01449v1 fatcat:jvo4pr5atvfb5kohpslvkhhmky
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